MétaCan
Menu
Back to cohort
Record W2101360293 · doi:10.1002/2013rs005354

Ionospheric tomography using ADS-B signals

2014· article· en· W2101360293 on OpenAlexaff
A. C. Cushley, Jean-Marc Noël

Bibliographic record

VenueRadio Science · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsTECTotal electron contentIonosphereTomographyAlgebraic Reconstruction TechniqueGeologyFaraday effectRay tracing (physics)Polarization (electrochemistry)Electron densityTransmitterRemote sensingPhysicsOpticsGeodesyComputer scienceGeophysicsElectronTelecommunications

Abstract

fetched live from OpenAlex

Numerical modeling has demonstrated that Automatic Dependent Surveillance Broadcast (ADS-B) signals can be used to reconstruct two-dimensional (2-D) electron density maps of the ionosphere using techniques for computerized tomography. Ray tracing techniques were used to determine the characteristics of individual waves, including the wave path and the state of polarization at the satellite receiver. The modeled Faraday rotation was computed and converted to total electron content (TEC) along the raypaths. The resulting TEC was used as input for computerized ionospheric tomography (CIT) using algebraic reconstruction technique. This study concentrated on reconstructing mesoscale structures 25–100 km in horizontal extent. The primary scientific interest of this study was to show that ADS-B signals can be used as a new source of data for CIT to image the ionosphere and to obtain a better understanding of magneto-ionic wave propagation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.230
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueRadio ScienceSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207